Hi Marco,
That's excellent, thanks for having another look. The newer
instructions you linked to are fine, but the old instructions for
submitting a package don't refer to the new upload method
(http://cygwin.com/setup.html). Not a big deal, but may cause some
confusion for other people adding new packages.
I'll mail my SSH public key to this list as per the instructions,
upload the packages once I get confirmation of my key, and send an
announcement to cygwin-announce once that's finished. Hopefully
that's it, but please let me know if there's anything else I should
do.
Thanks again,
Chris
On Fri, Jan 24, 2014 at 6:51 PM, Marco Atzeri marco.atz...@gmail.com wrote:
On 16/01/2014 03:36, Chris LeBlanc wrote:
Hi Marco,
Thanks for your feedback. I've rebuilt the packages for 2.2.1, and
also for Python 3.
I was running the unit test suite manually after installing h5py by
calling h5py.run_tests(). I've added this to the cygport files under
the src_test() function so we can now test using cygport. I had to
run the unit tests under the staging directory instead of the build
directory, because Cython builds the extension module .dll files in a
temporary build directory, therefore we can't import h5py from the
build dir. The tests pass for Python 2 and 3 for both 32 and 64 bit.
I've updated the files on the FTP site. Thanks for the one-line wget
command, the same command can be used to download the updated files:
wget -r -np -nH --cut-dirs=4
ftp://ftp.gns.cri.nz/pub/claritas/misc/cygwin_h5py_packages/
Thanks for working on the hdf5 package, I was pleased to see this in
Cygwin. I think it could be useful for quite a few packages.
Here is the setup.hint for the Python 3 version of h5py. The only
change here is switching from python/python-numpy to
python3/python3-numpy:
category: Python
requires: libhdf5_8 python3 python3-numpy hdf5
sdesc: A Pythonic interface to the HDF5 binary data format for Python 3
ldesc: This package lets you store huge amounts of numerical
data, and easily manipulate that data from NumPy. For example, you
can slice into multi-terabyte datasets stored on disk, as if they
were real NumPy arrays. Thousands of datasets can be stored in a
single file, categorized and tagged however you want.
Hopefully this is now ready to go. Please let me know if there's anything
else.
Thanks again,
Chris
for me it was already GTG (Good TO Go) and I had the impression also
Corinna had no objection.
Justed rebuilt your python-h5py-2.2.1-1 and it builds and packages fine.
please follow
https://sourceware.org/cygwin-apps/package-upload.html
for upload instruction
Regards
Marco
